1. Field of the Invention
The present invention relates to a method and a system for managing semiconductor manufacturing equipment.
2. Related Art
In management systems for manufacturing equipment, such as semiconductor manufacturing equipment, to decide whether or not the manufacturing equipment is operating normally, an interlock system is generally adopted which detects data on a parameter such as the temperature or pressure of the functional part of the manufacturing equipment, and stops the operation of the manufacturing equipment when the data indicates an abnormal value.
According to this interlock system, it is possible to immediately stop the operation of the manufacturing equipment when an abnormal data value is detected, and thereby minimize the ejection of nonconforming products.
However, in a conventional interlock system such as mentioned above, when the etching steps from beginning to end of etching work on a plasma etching system are controlled according to changes in the plasma emission intensity, the plasma emission intensity varies greatly from beginning to end of the etching process, so that the plasma emission intensity cannot be used as a parameter to detect a malfunction of the equipment.
In the interlock system, only one decision is made whether or not data values of a single parameter exceed a predetermined range, and it is impossible to capture time-series changes between data values. Therefore, it is not easy to catch a malfunction accurately.
Further, in the interlock system, even if a plurality of parameters are combined, it is hard to give correlation between the parameters, and it is difficult to catch a malfunction accurately.
The object of the present invention is to provide a management method and a management system capable of making a correct decision about a malfunction of the semiconductor manufacturing equipment.
According to the present invention, there is provided a method of managing the operation of semiconductor manufacturing equipment, comprising sampling a plurality of data of at least one parameter under the normal operating condition of the semiconductor manufacturing equipment, generating a Mahalanobis space from a group of sampled data, calculating a Mahalanobis distance from a group of measured values of the parameters, obtained under the operating condition of the semiconductor manufacturing equipment by using the Mahalanobis space, and when the Mahalanobis distance exceeds a predetermined value, making a decision that a malfunction occurred in the semiconductor manufacturing equipment.
In the management method according to the present invention, a Mahalanobis space based on a data group including a plurality of data is formed, and by using this Mahalanobis space, a Mahalanobis distance is calculated from measured values of the parameter, obtained in the operating condition of the semiconductor manufacturing equipment, and according to the values of the Mahalanobis distance, a decision is made whether the semiconductor manufacturing equipment is operating normally or abnormally.
The above-mentioned Mahalanobis space is expressed by an inverse matrix of a correlation matrix derived from an aggregate of data to be described later. Therefore, by using a Mahalanobis space, even though a single parameter is adopted, a data group is not handled as separate data values in the Mahalanobis space, and a correlation among data is taken into consideration.
Consequently, according to the management method of the present invention, a decision can be made whether the operating condition of the semiconductor manufacturing equipment is normal or not with high accuracy not obtainable in the prior-art interlock system, in which the correlation among data is not considered. For this reason, the operation of the semiconductor manufacturing equipment can be managed with much higher accuracy than in the prior art.
Further, a plurality of parameters other than said at least one parameter are provided. Mahalanobis spaces under abnormal conditions are formed previously, each abnormal condition being generated by setting one of said parameters at an abnormal value and other said parameters at normal values. When, from a value of said Mahalanobis distance, a decision has been made that a malfunction occurred, Mahalanobis distances corresponding to Mahalanobis spaces are calculated from said measured values by using said Mahalanobis space under the abnormal condition. And, it can be estimated that among said plurality of parameters, abnormality occurred in a parameter that gave a Mahalanobis space such that said Mahalanobis distance is closest to 1.
Among the Mahalanobis distances calculated on the basis of the respective Mahalanobis spaces, the Mahalanobis distance, which was calculated by using a Mahalanobis space under a condition closest to the present condition that gave a measured value, is closest to 1.
Therefore, among a plurality of Mahalanobis spaces used for calculating said Mahalanobis distances, a Mahalanobis space that brought forth a Mahalanobis distance closest to the value 1 can be regarded as the Mahalanobis space under a condition closest to the condition that brought forth the measured values. Accordingly, it can be estimated that the cause of abnormality lies in the abnormality detection parameter that brought about a Mahalanobis space that produces a Mahalanobis distance closest to the value 1.
The above-mentioned at least one parameter can be composed of a plurality of mutually different parameters. Therefore, Mahalanobis spaces can be formed at predetermined times from a data group of said plurality of parameters measured at predetermined times. Therefore, by using said Mahalanobis spaces formed at predetermined times, Mahalanobis distances at predetermined times can be calculated from a group of measured values of the plurality of parameters, obtained from the operating condition of the semiconductor manufacturing equipment.
According to the present invention, there is provided a management system of semiconductor manufacturing equipment comprising:
a memory unit for storing data on a Mahalanobis space obtained from a parameter showing a normal operating condition of semiconductor manufacturing equipment;
a detection mechanism for obtaining data values of said parameter from said semiconductor manufacturing equipment in operation;
an arithmetic circuit for calculating a Mahalanobis distance from a data group of said parameter, obtained by said detection mechanism by using said Mahalanobis space stored in said memory unit; and
a circuit for deciding whether or not a calculated value of said Mahalanobis distance by said arithmetic circuit exceeds a predetermined value.
According to the present invention, the arithmetic circuit calculates a Mahalanobis distance from parameter values obtained by the detection mechanism on the basis of a Mahalanobis space stored in the memory unit, and the decision circuit decides whether or not the calculated Mahalanobis distance exceeds a predetermined value. Therefore, the method according to the present invention can be put into quick and effective use.
The management system according to the present invention can be applied to management of the plasma etching system. As a detection mechanism for detecting plasma emission intensity of the plasma etching system, it is possible to use a detection mechanism with a plasma emission intensity detector that measures the intensity of a desired wavelength of plasma emission of the etching system.
From data on plasma emission intensity detected by the plasma emission intensity detector, it is possible to obtain the above-mentioned Mahalanobis space and Mahalanobis distance. By decision made by using the Mahalanobis distance, it is possible to decide with high accuracy whether the plasma etching system has operated normally or not.
The above-mentioned detection mechanism may be a detection mechanism including a voltage detector and a current detector for obtaining a current value, a voltage value, and a phase of high frequency output of a high frequency transmitter installed in the plasma etching system. By using this detection mechanism, in other words, by using data on the voltage value, the current value and the phase, the above-mentioned Mahalanobis space and Mahalanobis distance can be obtained. By a decision made by using the Mahalanobis distance, it is possible to decide with high accuracy whether the plasma etching system has operated normally or not.
The above-mentioned detection mechanism can further include a data converter for obtaining current values, voltage values and a phase from the fundamental wave and harmonics of the high frequency output from a current value and a voltage value detected by the current detector and the voltage detector. Therefore, on the basis of data of a plurality of parameters, such as the current values, voltage values and the phases of the fundamental wave and the harmonics at predetermined times, Mahalanobis spaces can be generated at the predetermined times. Thus, by using the Mahalanobis spaces at the predetermined times, Mahalanobis distances can be calculated from measured values of the plurality of parameters, obtained under operating condition of the etching system.
Therefore, by converting the operating condition of the etching system into time-series changes between data values by making a decision about the Mahalanobis distance generated in time series, the operating condition of the etching system can be managed suitably in time series according to the correlation among the parameters.
As the detection mechanism of the etching system, a detection mechanism with an emission spectrometer may be used to measure the intensity of a plurality of desired wavelengths of plasma emission.
The emission spectrometer, being capable of measuring the intensity of a plurality of desired wavelengths, can handle the different degrees of intensity of the plurality of wavelengths of plasma emission as parameters. Therefore, it becomes possible to suitably manage the operating condition of the etching system by time series control according to the correlation between the different degrees of intensity of different wavelengths of plasma emission.